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Mixture models and applications

Author: Nizar Bouguila; Wentao Fan
Publisher: Cham, Switzerland : Springer, [2020]
Series: Unsupervised and semi-supervised learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bouguila, Nizar
Mixture Models and Applications
Cham : Springer,c2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nizar Bouguila; Wentao Fan
ISBN: 9783030238766 3030238768
OCLC Number: 1112422502
Notes: Description based upon print version of record.
4.5.1.1 Experimental Framework and Results for VisTex Texture Dataset
Description: 1 online resource (356 pages).
Contents: Intro; Preface; References; Contents; Contributors; Part I Gaussian-Based Models; 1 A Gaussian Mixture Model Approach to ClassifyingResponse Types; 1.1 Background; 1.1.1 The Influence of Prior Information During Interrupted Visual Search; 1.1.2 Quantifying Individual Differences During the Interrupted Search Task; 1.1.3 An Alternative Approach to Classifying Response Types During Interrupted Search; 1.1.4 Aims of This Chapter; 1.2 Methods; 1.2.1 Data Collection; 1.2.2 Overview of Approach; 1.2.3 Gaussian Mixture Models; 1.2.4 Expectation-Maximisation Algorithm 1.2.5 Estimation of Mixture Model Parameters1.2.5.1 Initialisation; 1.2.5.2 Expectation Step; 1.2.5.3 Maximisation Step; 1.2.5.4 Convergence Criteria; 1.2.6 Log Probability Ratio; 1.3 Results; 1.3.1 Parameter Estimation of Response Distributions; 1.3.2 Evaluation of Previous Classification Criteria; 1.3.3 Comparison of Classification Methods; 1.4 Discussion; Appendix: Additional Methods; Participants; Stimuli Presentation; Procedure; References; 2 Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures; 2.1 Introduction; 2.2 Background; 2.3 Trajectory Generation 2.3.1 Dynamical System2.3.2 Optimization Objective; 2.3.3 Tracking Formulation; 2.3.4 Stochastic Solution; 2.3.5 Periodic Motions; 2.4 User Interface; 2.4.1 Semi-tied Structure; 2.5 Conclusions; References; 3 Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series; 3.1 Introduction; 3.2 Movement Primitives; 3.2.1 Radial Basis Functions (RBFs); 3.2.1.1 Gaussian Mixture Regression (GMR); 3.2.2 Bernstein Basis Functions; 3.2.3 Fourier Basis Functions; 3.2.4 Ergodic Control 4.2.3 Mixture of Bounded Asymmetric Gaussian Distribution for Multidimensional Data4.2.4 Parameters Learning; 4.2.4.1 Mixing Parameter Estimation; 4.2.4.2 Mean Parameter Estimation; 4.2.4.3 Left Standard Deviation Estimation; 4.2.4.4 Right Standard Deviation Estimation; 4.3 Textual Spam Detection; 4.4 Object Categorization via Bounded Asymmetric Gaussian Mixture Model; 4.4.1 Experiments and Results; 4.4.1.1 Experimental Framework and Results: Caltech 101 Dataset; 4.4.1.2 Experimental Framework and Results: Corel Dataset; 4.5 Texture Image Clustering; 4.5.1 Experiments and Results
Series Title: Unsupervised and semi-supervised learning.
Responsibility: Nizar Bouguila, Wentao Fan, editors.

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